Overview

Dataset statistics

Number of variables19
Number of observations52325
Missing cells349292
Missing cells (%)35.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.6 MiB
Average record size in memory152.0 B

Variable types

Text2
Categorical3
Numeric13
Boolean1

Alerts

MRG has constant value ""Constant
TENURE is highly imbalanced (87.4%)Imbalance
REGION has 20576 (39.3%) missing valuesMissing
MONTANT has 18400 (35.2%) missing valuesMissing
FREQUENCE_RECH has 18400 (35.2%) missing valuesMissing
REVENUE has 17687 (33.8%) missing valuesMissing
ARPU_SEGMENT has 17687 (33.8%) missing valuesMissing
FREQUENCE has 17687 (33.8%) missing valuesMissing
DATA_VOLUME has 25647 (49.0%) missing valuesMissing
ON_NET has 19155 (36.6%) missing valuesMissing
ORANGE has 21760 (41.6%) missing valuesMissing
TIGO has 31265 (59.8%) missing valuesMissing
ZONE1 has 48134 (92.0%) missing valuesMissing
ZONE2 has 48965 (93.6%) missing valuesMissing
TOP_PACK has 21963 (42.0%) missing valuesMissing
FREQ_TOP_PACK has 21963 (42.0%) missing valuesMissing
DATA_VOLUME is highly skewed (γ1 = 32.65507376)Skewed
user_id has unique valuesUnique
DATA_VOLUME has 7822 (14.9%) zerosZeros
ON_NET has 2637 (5.0%) zerosZeros
ORANGE has 1553 (3.0%) zerosZeros
TIGO has 2234 (4.3%) zerosZeros
ZONE1 has 1460 (2.8%) zerosZeros
ZONE2 has 989 (1.9%) zerosZeros

Reproduction

Analysis started2024-04-24 12:54:45.505045
Analysis finished2024-04-24 12:55:33.132735
Duration47.63 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

user_id
Text

UNIQUE 

Distinct52325
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size408.9 KiB
2024-04-24T12:55:33.570663image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length40
Median length40
Mean length40
Min length40

Characters and Unicode

Total characters2093000
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52325 ?
Unique (%)100.0%

Sample

1st row00000bfd7d50f01092811bc0c8d7b0d6fe7c3596
2nd row00000cb4a5d760de88fecb38e2f71b7bec52e834
3rd row00001654a9d9f96303d9969d0a4a851714a4bb57
4th row00001dd6fa45f7ba044bd5d84937be464ce78ac2
5th row000028d9e13a595abe061f9b58f3d76ab907850f
ValueCountFrequency (%)
00000bfd7d50f01092811bc0c8d7b0d6fe7c3596 1
 
< 0.1%
0000527d276a6ba8b02810cc2c1d60d25e650f5f 1
 
< 0.1%
0000cd42663b7542ccac678690d07c73179a5268 1
 
< 0.1%
0000c0f0fd1a7b922b099a0c5434fa5fff9a6f44 1
 
< 0.1%
00001654a9d9f96303d9969d0a4a851714a4bb57 1
 
< 0.1%
00001dd6fa45f7ba044bd5d84937be464ce78ac2 1
 
< 0.1%
000028d9e13a595abe061f9b58f3d76ab907850f 1
 
< 0.1%
0000296564272665ccd2925d377e124f3306b01e 1
 
< 0.1%
00002b0ed56e2c199ec8c3021327229afa70f063 1
 
< 0.1%
0000313946b6849745963442c6e572d47cd24ced 1
 
< 0.1%
Other values (52315) 52315
> 99.9%
2024-04-24T12:55:34.558971image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 185601
 
8.9%
3 133397
 
6.4%
1 133026
 
6.4%
2 132967
 
6.4%
4 132817
 
6.3%
5 132422
 
6.3%
6 126317
 
6.0%
c 124364
 
5.9%
7 124289
 
5.9%
9 124276
 
5.9%
Other values (6) 743524
35.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1349220
64.5%
Lowercase Letter 743780
35.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 185601
13.8%
3 133397
9.9%
1 133026
9.9%
2 132967
9.9%
4 132817
9.8%
5 132422
9.8%
6 126317
9.4%
7 124289
9.2%
9 124276
9.2%
8 124108
9.2%
Lowercase Letter
ValueCountFrequency (%)
c 124364
16.7%
b 124269
16.7%
d 123947
16.7%
e 123902
16.7%
a 123836
16.6%
f 123462
16.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1349220
64.5%
Latin 743780
35.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 185601
13.8%
3 133397
9.9%
1 133026
9.9%
2 132967
9.9%
4 132817
9.8%
5 132422
9.8%
6 126317
9.4%
7 124289
9.2%
9 124276
9.2%
8 124108
9.2%
Latin
ValueCountFrequency (%)
c 124364
16.7%
b 124269
16.7%
d 123947
16.7%
e 123902
16.7%
a 123836
16.6%
f 123462
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2093000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 185601
 
8.9%
3 133397
 
6.4%
1 133026
 
6.4%
2 132967
 
6.4%
4 132817
 
6.3%
5 132422
 
6.3%
6 126317
 
6.0%
c 124364
 
5.9%
7 124289
 
5.9%
9 124276
 
5.9%
Other values (6) 743524
35.5%

REGION
Categorical

MISSING 

Distinct14
Distinct (%)< 0.1%
Missing20576
Missing (%)39.3%
Memory size408.9 KiB
DAKAR
12526 
THIES
4457 
SAINT-LOUIS
2840 
LOUGA
2408 
KAOLACK
2351 
Other values (9)
7167 

Length

Max length11
Median length5
Mean length6.3128602
Min length5

Characters and Unicode

Total characters200427
Distinct characters20
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFATICK
2nd rowDAKAR
3rd rowDAKAR
4th rowLOUGA
5th rowLOUGA

Common Values

ValueCountFrequency (%)
DAKAR 12526
23.9%
THIES 4457
 
8.5%
SAINT-LOUIS 2840
 
5.4%
LOUGA 2408
 
4.6%
KAOLACK 2351
 
4.5%
DIOURBEL 1652
 
3.2%
TAMBACOUNDA 1326
 
2.5%
KAFFRINE 1053
 
2.0%
FATICK 920
 
1.8%
KOLDA 889
 
1.7%
Other values (4) 1327
 
2.5%
(Missing) 20576
39.3%

Length

2024-04-24T12:55:34.778559image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dakar 12526
39.5%
thies 4457
 
14.0%
saint-louis 2840
 
8.9%
louga 2408
 
7.6%
kaolack 2351
 
7.4%
diourbel 1652
 
5.2%
tambacounda 1326
 
4.2%
kaffrine 1053
 
3.3%
fatick 920
 
2.9%
kolda 889
 
2.8%
Other values (4) 1327
 
4.2%

Most occurring characters

ValueCountFrequency (%)
A 43212
21.6%
K 20117
10.0%
D 16489
 
8.2%
R 15777
 
7.9%
I 14923
 
7.4%
O 12135
 
6.1%
T 10228
 
5.1%
S 10206
 
5.1%
L 10140
 
5.1%
U 8895
 
4.4%
Other values (10) 38305
19.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 197587
98.6%
Dash Punctuation 2840
 
1.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 43212
21.9%
K 20117
10.2%
D 16489
 
8.3%
R 15777
 
8.0%
I 14923
 
7.6%
O 12135
 
6.1%
T 10228
 
5.2%
S 10206
 
5.2%
L 10140
 
5.1%
U 8895
 
4.5%
Other values (9) 35465
17.9%
Dash Punctuation
ValueCountFrequency (%)
- 2840
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 197587
98.6%
Common 2840
 
1.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 43212
21.9%
K 20117
10.2%
D 16489
 
8.3%
R 15777
 
8.0%
I 14923
 
7.6%
O 12135
 
6.1%
T 10228
 
5.2%
S 10206
 
5.2%
L 10140
 
5.1%
U 8895
 
4.5%
Other values (9) 35465
17.9%
Common
ValueCountFrequency (%)
- 2840
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 200427
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 43212
21.6%
K 20117
10.0%
D 16489
 
8.2%
R 15777
 
7.9%
I 14923
 
7.4%
O 12135
 
6.1%
T 10228
 
5.1%
S 10206
 
5.1%
L 10140
 
5.1%
U 8895
 
4.4%
Other values (10) 38305
19.1%

TENURE
Categorical

IMBALANCE 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size408.9 KiB
K > 24 month
49699 
I 18-21 month
 
1042
H 15-18 month
 
606
G 12-15 month
 
364
J 21-24 month
 
317
Other values (4)
 
297

Length

Max length13
Median length12
Mean length12.042962
Min length3

Characters and Unicode

Total characters630148
Distinct characters24
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowK > 24 month
2nd rowI 18-21 month
3rd rowK > 24 month
4th rowK > 24 month
5th rowK > 24 month

Common Values

ValueCountFrequency (%)
K > 24 month 49699
95.0%
I 18-21 month 1042
 
2.0%
H 15-18 month 606
 
1.2%
G 12-15 month 364
 
0.7%
J 21-24 month 317
 
0.6%
F 9-12 month 224
 
0.4%
E 6-9 month 50
 
0.1%
D 3-6 month 22
 
< 0.1%
K > 1
 
< 0.1%

Length

2024-04-24T12:55:34.998089image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-24T12:55:35.233137image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
month 52324
25.3%
k 49700
24.0%
49700
24.0%
24 49699
24.0%
i 1042
 
0.5%
18-21 1042
 
0.5%
h 606
 
0.3%
15-18 606
 
0.3%
12-15 364
 
0.2%
g 364
 
0.2%
Other values (8) 1226
 
0.6%

Most occurring characters

ValueCountFrequency (%)
154348
24.5%
m 52324
 
8.3%
o 52324
 
8.3%
n 52324
 
8.3%
t 52324
 
8.3%
h 52324
 
8.3%
2 51963
 
8.2%
4 50016
 
7.9%
K 49700
 
7.9%
> 49700
 
7.9%
Other values (14) 12801
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 261620
41.5%
Space Separator 154348
24.5%
Decimal Number 109530
17.4%
Uppercase Letter 52325
 
8.3%
Math Symbol 49700
 
7.9%
Dash Punctuation 2625
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 51963
47.4%
4 50016
45.7%
1 4565
 
4.2%
8 1648
 
1.5%
5 970
 
0.9%
9 274
 
0.3%
6 72
 
0.1%
3 22
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
K 49700
95.0%
I 1042
 
2.0%
H 606
 
1.2%
G 364
 
0.7%
J 317
 
0.6%
F 224
 
0.4%
E 50
 
0.1%
D 22
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
m 52324
20.0%
o 52324
20.0%
n 52324
20.0%
t 52324
20.0%
h 52324
20.0%
Space Separator
ValueCountFrequency (%)
154348
100.0%
Math Symbol
ValueCountFrequency (%)
> 49700
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2625
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 316203
50.2%
Latin 313945
49.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
m 52324
16.7%
o 52324
16.7%
n 52324
16.7%
t 52324
16.7%
h 52324
16.7%
K 49700
15.8%
I 1042
 
0.3%
H 606
 
0.2%
G 364
 
0.1%
J 317
 
0.1%
Other values (3) 296
 
0.1%
Common
ValueCountFrequency (%)
154348
48.8%
2 51963
 
16.4%
4 50016
 
15.8%
> 49700
 
15.7%
1 4565
 
1.4%
- 2625
 
0.8%
8 1648
 
0.5%
5 970
 
0.3%
9 274
 
0.1%
6 72
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 630148
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
154348
24.5%
m 52324
 
8.3%
o 52324
 
8.3%
n 52324
 
8.3%
t 52324
 
8.3%
h 52324
 
8.3%
2 51963
 
8.2%
4 50016
 
7.9%
K 49700
 
7.9%
> 49700
 
7.9%
Other values (14) 12801
 
2.0%

MONTANT
Real number (ℝ)

MISSING 

Distinct1002
Distinct (%)3.0%
Missing18400
Missing (%)35.2%
Infinite0
Infinite (%)0.0%
Mean5597.3932
Minimum50
Maximum120870
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size408.9 KiB
2024-04-24T12:55:35.562436image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile250
Q11000
median3000
Q37500
95-th percentile18800
Maximum120870
Range120820
Interquartile range (IQR)6500

Descriptive statistics

Standard deviation7196.8937
Coefficient of variation (CV)1.2857581
Kurtosis24.589717
Mean5597.3932
Median Absolute Deviation (MAD)2450
Skewness3.5748256
Sum1.8989157 × 108
Variance51795279
MonotonicityNot monotonic
2024-04-24T12:55:35.797586image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500 2712
 
5.2%
1000 2040
 
3.9%
1500 1132
 
2.2%
2000 1079
 
2.1%
200 1043
 
2.0%
3000 828
 
1.6%
2500 809
 
1.5%
4000 627
 
1.2%
3500 583
 
1.1%
100 499
 
1.0%
Other values (992) 22573
43.1%
(Missing) 18400
35.2%
ValueCountFrequency (%)
50 10
 
< 0.1%
100 499
1.0%
130 1
 
< 0.1%
150 48
 
0.1%
200 1043
2.0%
250 231
 
0.4%
300 289
 
0.6%
350 54
 
0.1%
400 334
 
0.6%
450 60
 
0.1%
ValueCountFrequency (%)
120870 1
< 0.1%
120000 1
< 0.1%
119450 1
< 0.1%
114400 1
< 0.1%
106900 1
< 0.1%
93000 1
< 0.1%
91500 2
< 0.1%
89500 1
< 0.1%
89000 1
< 0.1%
88700 1
< 0.1%

FREQUENCE_RECH
Real number (ℝ)

MISSING 

Distinct100
Distinct (%)0.3%
Missing18400
Missing (%)35.2%
Infinite0
Infinite (%)0.0%
Mean11.668651
Minimum1
Maximum106
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size408.9 KiB
2024-04-24T12:55:36.127321image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q316
95-th percentile40
Maximum106
Range105
Interquartile range (IQR)14

Descriptive statistics

Standard deviation13.418135
Coefficient of variation (CV)1.1499302
Kurtosis5.1744832
Mean11.668651
Median Absolute Deviation (MAD)5
Skewness2.0924059
Sum395859
Variance180.04635
MonotonicityNot monotonic
2024-04-24T12:55:36.629396image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 5369
 
10.3%
2 3293
 
6.3%
3 2611
 
5.0%
4 2130
 
4.1%
5 1831
 
3.5%
6 1593
 
3.0%
7 1352
 
2.6%
8 1243
 
2.4%
9 1042
 
2.0%
10 965
 
1.8%
Other values (90) 12496
23.9%
(Missing) 18400
35.2%
ValueCountFrequency (%)
1 5369
10.3%
2 3293
6.3%
3 2611
5.0%
4 2130
 
4.1%
5 1831
 
3.5%
6 1593
 
3.0%
7 1352
 
2.6%
8 1243
 
2.4%
9 1042
 
2.0%
10 965
 
1.8%
ValueCountFrequency (%)
106 2
< 0.1%
103 1
 
< 0.1%
98 1
 
< 0.1%
97 2
< 0.1%
96 1
 
< 0.1%
95 1
 
< 0.1%
94 1
 
< 0.1%
93 2
< 0.1%
92 3
< 0.1%
91 1
 
< 0.1%

REVENUE
Real number (ℝ)

MISSING 

Distinct9678
Distinct (%)27.9%
Missing17687
Missing (%)33.8%
Infinite0
Infinite (%)0.0%
Mean5578.589
Minimum1
Maximum147739
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size408.9 KiB
2024-04-24T12:55:36.942569image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile199
Q11000
median3000
Q37499
95-th percentile19001
Maximum147739
Range147738
Interquartile range (IQR)6499

Descriptive statistics

Standard deviation7296.1927
Coefficient of variation (CV)1.3078921
Kurtosis25.983209
Mean5578.589
Median Absolute Deviation (MAD)2499
Skewness3.6018265
Sum1.9323116 × 108
Variance53234428
MonotonicityNot monotonic
2024-04-24T12:55:37.177764image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500 1411
 
2.7%
1000 898
 
1.7%
200 504
 
1.0%
1500 478
 
0.9%
2000 427
 
0.8%
3000 326
 
0.6%
2500 303
 
0.6%
4000 212
 
0.4%
3500 205
 
0.4%
100 178
 
0.3%
Other values (9668) 29696
56.8%
(Missing) 17687
33.8%
ValueCountFrequency (%)
1 115
0.2%
2 67
0.1%
3 8
 
< 0.1%
4 51
0.1%
5 4
 
< 0.1%
6 25
 
< 0.1%
7 9
 
< 0.1%
8 32
 
0.1%
9 30
 
0.1%
10 76
0.1%
ValueCountFrequency (%)
147739 1
< 0.1%
126314 1
< 0.1%
124000 1
< 0.1%
108216 1
< 0.1%
96959 1
< 0.1%
93195 1
< 0.1%
93001 1
< 0.1%
90067 1
< 0.1%
89502 1
< 0.1%
89224 1
< 0.1%

ARPU_SEGMENT
Real number (ℝ)

MISSING 

Distinct5730
Distinct (%)16.5%
Missing17687
Missing (%)33.8%
Infinite0
Infinite (%)0.0%
Mean1859.5349
Minimum0
Maximum49246
Zeros115
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size408.9 KiB
2024-04-24T12:55:37.460188image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile66
Q1333
median1000
Q32500
95-th percentile6334
Maximum49246
Range49246
Interquartile range (IQR)2167

Descriptive statistics

Standard deviation2432.0598
Coefficient of variation (CV)1.307886
Kurtosis25.983376
Mean1859.5349
Median Absolute Deviation (MAD)833
Skewness3.6018465
Sum64410571
Variance5914914.8
MonotonicityNot monotonic
2024-04-24T12:55:37.944503image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
167 1625
 
3.1%
333 1089
 
2.1%
500 668
 
1.3%
67 570
 
1.1%
667 532
 
1.0%
1000 444
 
0.8%
833 374
 
0.7%
1333 276
 
0.5%
1167 272
 
0.5%
233 230
 
0.4%
Other values (5720) 28558
54.6%
(Missing) 17687
33.8%
ValueCountFrequency (%)
0 115
0.2%
1 126
0.2%
2 38
 
0.1%
3 138
0.3%
4 73
0.1%
5 47
 
0.1%
6 22
 
< 0.1%
7 91
0.2%
8 18
 
< 0.1%
9 26
 
< 0.1%
ValueCountFrequency (%)
49246 1
< 0.1%
42105 1
< 0.1%
41333 1
< 0.1%
36072 1
< 0.1%
32320 1
< 0.1%
31065 1
< 0.1%
31000 1
< 0.1%
30022 1
< 0.1%
29834 1
< 0.1%
29741 1
< 0.1%

FREQUENCE
Real number (ℝ)

MISSING 

Distinct91
Distinct (%)0.3%
Missing17687
Missing (%)33.8%
Infinite0
Infinite (%)0.0%
Mean14.111958
Minimum1
Maximum91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size408.9 KiB
2024-04-24T12:55:38.148400image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median9
Q320
95-th percentile46
Maximum91
Range90
Interquartile range (IQR)17

Descriptive statistics

Standard deviation14.863632
Coefficient of variation (CV)1.053265
Kurtosis3.3860032
Mean14.111958
Median Absolute Deviation (MAD)7
Skewness1.7760738
Sum488810
Variance220.92756
MonotonicityNot monotonic
2024-04-24T12:55:38.747589image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3942
 
7.5%
2 2758
 
5.3%
3 2293
 
4.4%
4 1983
 
3.8%
5 1731
 
3.3%
6 1532
 
2.9%
7 1425
 
2.7%
8 1233
 
2.4%
9 1198
 
2.3%
10 1031
 
2.0%
Other values (81) 15512
29.6%
(Missing) 17687
33.8%
ValueCountFrequency (%)
1 3942
7.5%
2 2758
5.3%
3 2293
4.4%
4 1983
3.8%
5 1731
3.3%
6 1532
 
2.9%
7 1425
 
2.7%
8 1233
 
2.4%
9 1198
 
2.3%
10 1031
 
2.0%
ValueCountFrequency (%)
91 1
 
< 0.1%
90 1
 
< 0.1%
89 7
< 0.1%
88 4
 
< 0.1%
87 8
< 0.1%
86 9
< 0.1%
85 9
< 0.1%
84 5
 
< 0.1%
83 12
< 0.1%
82 15
< 0.1%

DATA_VOLUME
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct7971
Distinct (%)29.9%
Missing25647
Missing (%)49.0%
Infinite0
Infinite (%)0.0%
Mean3548.6737
Minimum0
Maximum926547
Zeros7822
Zeros (%)14.9%
Negative0
Negative (%)0.0%
Memory size408.9 KiB
2024-04-24T12:55:39.540220image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median276
Q32981
95-th percentile15388.9
Maximum926547
Range926547
Interquartile range (IQR)2981

Descriptive statistics

Standard deviation15318.304
Coefficient of variation (CV)4.3166278
Kurtosis1545.23
Mean3548.6737
Median Absolute Deviation (MAD)276
Skewness32.655074
Sum94671518
Variance2.3465043 × 108
MonotonicityNot monotonic
2024-04-24T12:55:40.434124image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7822
 
14.9%
1 970
 
1.9%
2 323
 
0.6%
3 173
 
0.3%
4 158
 
0.3%
1024 138
 
0.3%
5 112
 
0.2%
6 101
 
0.2%
1023 94
 
0.2%
7 88
 
0.2%
Other values (7961) 16699
31.9%
(Missing) 25647
49.0%
ValueCountFrequency (%)
0 7822
14.9%
1 970
 
1.9%
2 323
 
0.6%
3 173
 
0.3%
4 158
 
0.3%
5 112
 
0.2%
6 101
 
0.2%
7 88
 
0.2%
8 69
 
0.1%
9 81
 
0.2%
ValueCountFrequency (%)
926547 1
< 0.1%
867127 1
< 0.1%
752018 1
< 0.1%
720309 1
< 0.1%
611581 1
< 0.1%
576214 1
< 0.1%
490458 1
< 0.1%
443833 1
< 0.1%
434373 1
< 0.1%
322763 1
< 0.1%

ON_NET
Real number (ℝ)

MISSING  ZEROS 

Distinct2578
Distinct (%)7.8%
Missing19155
Missing (%)36.6%
Infinite0
Infinite (%)0.0%
Mean278.24787
Minimum0
Maximum23595
Zeros2637
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size408.9 KiB
2024-04-24T12:55:40.983238image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median27
Q3157
95-th percentile1340
Maximum23595
Range23595
Interquartile range (IQR)152

Descriptive statistics

Standard deviation867.60815
Coefficient of variation (CV)3.1181124
Kurtosis86.87487
Mean278.24787
Median Absolute Deviation (MAD)26
Skewness7.4879948
Sum9229482
Variance752743.91
MonotonicityNot monotonic
2024-04-24T12:55:41.800426image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2637
 
5.0%
1 2226
 
4.3%
2 1389
 
2.7%
7 1038
 
2.0%
8 996
 
1.9%
3 989
 
1.9%
4 943
 
1.8%
6 728
 
1.4%
5 692
 
1.3%
9 465
 
0.9%
Other values (2568) 21067
40.3%
(Missing) 19155
36.6%
ValueCountFrequency (%)
0 2637
5.0%
1 2226
4.3%
2 1389
2.7%
3 989
 
1.9%
4 943
 
1.8%
5 692
 
1.3%
6 728
 
1.4%
7 1038
 
2.0%
8 996
 
1.9%
9 465
 
0.9%
ValueCountFrequency (%)
23595 1
< 0.1%
21480 1
< 0.1%
17400 1
< 0.1%
15175 1
< 0.1%
14972 1
< 0.1%
14645 1
< 0.1%
14506 1
< 0.1%
13683 1
< 0.1%
13167 1
< 0.1%
12864 1
< 0.1%

ORANGE
Real number (ℝ)

MISSING  ZEROS 

Distinct1099
Distinct (%)3.6%
Missing21760
Missing (%)41.6%
Infinite0
Infinite (%)0.0%
Mean95.388353
Minimum0
Maximum5841
Zeros1553
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size408.9 KiB
2024-04-24T12:55:42.108347image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median29
Q397
95-th percentile393
Maximum5841
Range5841
Interquartile range (IQR)90

Descriptive statistics

Standard deviation204.04721
Coefficient of variation (CV)2.1391208
Kurtosis83.48438
Mean95.388353
Median Absolute Deviation (MAD)27
Skewness6.9106027
Sum2915545
Variance41635.262
MonotonicityNot monotonic
2024-04-24T12:55:42.498815image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1675
 
3.2%
0 1553
 
3.0%
2 1189
 
2.3%
3 876
 
1.7%
4 818
 
1.6%
8 613
 
1.2%
5 559
 
1.1%
6 557
 
1.1%
7 546
 
1.0%
10 497
 
0.9%
Other values (1089) 21682
41.4%
(Missing) 21760
41.6%
ValueCountFrequency (%)
0 1553
3.0%
1 1675
3.2%
2 1189
2.3%
3 876
1.7%
4 818
1.6%
5 559
 
1.1%
6 557
 
1.1%
7 546
 
1.0%
8 613
 
1.2%
9 466
 
0.9%
ValueCountFrequency (%)
5841 1
< 0.1%
4196 1
< 0.1%
4185 1
< 0.1%
3631 1
< 0.1%
3525 1
< 0.1%
3457 1
< 0.1%
3330 1
< 0.1%
3284 1
< 0.1%
3280 1
< 0.1%
3205 1
< 0.1%

TIGO
Real number (ℝ)

MISSING  ZEROS 

Distinct422
Distinct (%)2.0%
Missing31265
Missing (%)59.8%
Infinite0
Infinite (%)0.0%
Mean23.246819
Minimum0
Maximum2663
Zeros2234
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size408.9 KiB
2024-04-24T12:55:42.969627image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q320
95-th percentile96
Maximum2663
Range2663
Interquartile range (IQR)18

Descriptive statistics

Standard deviation63.873315
Coefficient of variation (CV)2.7476153
Kurtosis275.73245
Mean23.246819
Median Absolute Deviation (MAD)5
Skewness12.070005
Sum489578
Variance4079.8003
MonotonicityNot monotonic
2024-04-24T12:55:43.345763image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2786
 
5.3%
0 2234
 
4.3%
2 1813
 
3.5%
3 1314
 
2.5%
4 1085
 
2.1%
5 812
 
1.6%
6 733
 
1.4%
7 630
 
1.2%
8 610
 
1.2%
9 507
 
1.0%
Other values (412) 8536
 
16.3%
(Missing) 31265
59.8%
ValueCountFrequency (%)
0 2234
4.3%
1 2786
5.3%
2 1813
3.5%
3 1314
2.5%
4 1085
 
2.1%
5 812
 
1.6%
6 733
 
1.4%
7 630
 
1.2%
8 610
 
1.2%
9 507
 
1.0%
ValueCountFrequency (%)
2663 1
< 0.1%
1651 1
< 0.1%
1594 1
< 0.1%
1512 1
< 0.1%
1476 1
< 0.1%
1379 1
< 0.1%
1374 1
< 0.1%
1317 1
< 0.1%
1240 1
< 0.1%
1160 1
< 0.1%

ZONE1
Real number (ℝ)

MISSING  ZEROS 

Distinct147
Distinct (%)3.5%
Missing48134
Missing (%)92.0%
Infinite0
Infinite (%)0.0%
Mean8.7625865
Minimum0
Maximum1427
Zeros1460
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size408.9 KiB
2024-04-24T12:55:43.751745image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile32
Maximum1427
Range1427
Interquartile range (IQR)4

Descriptive statistics

Standard deviation44.152445
Coefficient of variation (CV)5.0387457
Kurtosis376.42957
Mean8.7625865
Median Absolute Deviation (MAD)1
Skewness16.151948
Sum36724
Variance1949.4384
MonotonicityNot monotonic
2024-04-24T12:55:44.519587image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1460
 
2.8%
1 1027
 
2.0%
2 394
 
0.8%
3 235
 
0.4%
4 146
 
0.3%
5 124
 
0.2%
6 81
 
0.2%
7 61
 
0.1%
9 60
 
0.1%
8 54
 
0.1%
Other values (137) 549
 
1.0%
(Missing) 48134
92.0%
ValueCountFrequency (%)
0 1460
2.8%
1 1027
2.0%
2 394
 
0.8%
3 235
 
0.4%
4 146
 
0.3%
5 124
 
0.2%
6 81
 
0.2%
7 61
 
0.1%
8 54
 
0.1%
9 60
 
0.1%
ValueCountFrequency (%)
1427 1
< 0.1%
963 1
< 0.1%
820 1
< 0.1%
673 1
< 0.1%
627 1
< 0.1%
554 1
< 0.1%
506 1
< 0.1%
463 1
< 0.1%
422 1
< 0.1%
406 1
< 0.1%

ZONE2
Real number (ℝ)

MISSING  ZEROS 

Distinct110
Distinct (%)3.3%
Missing48965
Missing (%)93.6%
Infinite0
Infinite (%)0.0%
Mean7.5291667
Minimum0
Maximum932
Zeros989
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size408.9 KiB
2024-04-24T12:55:45.147356image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q35
95-th percentile29
Maximum932
Range932
Interquartile range (IQR)5

Descriptive statistics

Standard deviation28.504446
Coefficient of variation (CV)3.78587
Kurtosis412.87348
Mean7.5291667
Median Absolute Deviation (MAD)2
Skewness16.335985
Sum25298
Variance812.50347
MonotonicityNot monotonic
2024-04-24T12:55:45.476720image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 989
 
1.9%
1 659
 
1.3%
2 366
 
0.7%
3 209
 
0.4%
4 191
 
0.4%
5 129
 
0.2%
6 101
 
0.2%
7 69
 
0.1%
8 67
 
0.1%
9 61
 
0.1%
Other values (100) 519
 
1.0%
(Missing) 48965
93.6%
ValueCountFrequency (%)
0 989
1.9%
1 659
1.3%
2 366
 
0.7%
3 209
 
0.4%
4 191
 
0.4%
5 129
 
0.2%
6 101
 
0.2%
7 69
 
0.1%
8 67
 
0.1%
9 61
 
0.1%
ValueCountFrequency (%)
932 1
< 0.1%
541 1
< 0.1%
527 1
< 0.1%
279 1
< 0.1%
258 1
< 0.1%
252 1
< 0.1%
246 1
< 0.1%
235 1
< 0.1%
231 1
< 0.1%
215 2
< 0.1%

MRG
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size102.3 KiB
False
52324 
(Missing)
 
1
ValueCountFrequency (%)
False 52324
> 99.9%
(Missing) 1
 
< 0.1%
2024-04-24T12:55:45.711896image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

REGULARITY
Real number (ℝ)

Distinct62
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean28.077785
Minimum1
Maximum62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size408.9 KiB
2024-04-24T12:55:45.947444image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median24
Q351
95-th percentile62
Maximum62
Range61
Interquartile range (IQR)45

Descriptive statistics

Standard deviation22.330879
Coefficient of variation (CV)0.79532196
Kurtosis-1.490132
Mean28.077785
Median Absolute Deviation (MAD)20
Skewness0.24649481
Sum1469142
Variance498.66815
MonotonicityNot monotonic
2024-04-24T12:55:46.276245image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 4737
 
9.1%
62 4124
 
7.9%
2 2894
 
5.5%
3 2059
 
3.9%
4 1680
 
3.2%
61 1562
 
3.0%
5 1396
 
2.7%
6 1292
 
2.5%
60 1207
 
2.3%
7 1105
 
2.1%
Other values (52) 30268
57.8%
ValueCountFrequency (%)
1 4737
9.1%
2 2894
5.5%
3 2059
3.9%
4 1680
 
3.2%
5 1396
 
2.7%
6 1292
 
2.5%
7 1105
 
2.1%
8 990
 
1.9%
9 864
 
1.7%
10 822
 
1.6%
ValueCountFrequency (%)
62 4124
7.9%
61 1562
 
3.0%
60 1207
 
2.3%
59 1000
 
1.9%
58 808
 
1.5%
57 772
 
1.5%
56 703
 
1.3%
55 635
 
1.2%
54 615
 
1.2%
53 614
 
1.2%

TOP_PACK
Text

MISSING 

Distinct88
Distinct (%)0.3%
Missing21963
Missing (%)42.0%
Memory size408.9 KiB
2024-04-24T12:55:46.652847image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length49
Median length42
Mean length23.180324
Min length9

Characters and Unicode

Total characters703801
Distinct characters70
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)< 0.1%

Sample

1st rowOn net 200F=Unlimited _call24H
2nd rowOn-net 1000F=10MilF;10d
3rd rowData:1000F=5GB,7d
4th rowMixt 250F=Unlimited_call24H
5th rowMIXT:500F= 2500F on net _2500F off net;2d
ValueCountFrequency (%)
all-net 9254
 
12.2%
500f=2000f;5d 7543
 
9.9%
net 6260
 
8.3%
on 5824
 
7.7%
200f=unlimited 3726
 
4.9%
call24h 3726
 
4.9%
2500f 3242
 
4.3%
data 3150
 
4.2%
data:490f=1gb,7d 2876
 
3.8%
mixt 2202
 
2.9%
Other values (121) 28053
37.0%
2024-04-24T12:55:47.456283image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 98753
 
14.0%
45494
 
6.5%
l 42419
 
6.0%
F 40898
 
5.8%
t 39233
 
5.6%
n 35161
 
5.0%
2 33085
 
4.7%
e 28318
 
4.0%
a 27364
 
3.9%
5 26640
 
3.8%
Other values (60) 286436
40.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 252515
35.9%
Decimal Number 194888
27.7%
Uppercase Letter 124287
17.7%
Space Separator 45494
 
6.5%
Other Punctuation 32013
 
4.5%
Math Symbol 26597
 
3.8%
Connector Punctuation 15824
 
2.2%
Dash Punctuation 11141
 
1.6%
Close Punctuation 434
 
0.1%
Open Punctuation 434
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 40898
32.9%
D 11476
 
9.2%
A 10929
 
8.8%
H 9495
 
7.6%
B 8301
 
6.7%
M 8009
 
6.4%
U 7550
 
6.1%
O 5894
 
4.7%
G 5510
 
4.4%
I 3337
 
2.7%
Other values (15) 12888
 
10.4%
Lowercase Letter
ValueCountFrequency (%)
l 42419
16.8%
t 39233
15.5%
n 35161
13.9%
e 28318
11.2%
a 27364
10.8%
d 24236
9.6%
i 19679
7.8%
o 8054
 
3.2%
m 7723
 
3.1%
c 6185
 
2.4%
Other values (12) 14143
 
5.6%
Decimal Number
ValueCountFrequency (%)
0 98753
50.7%
2 33085
 
17.0%
5 26640
 
13.7%
4 15380
 
7.9%
1 10753
 
5.5%
3 3377
 
1.7%
7 3300
 
1.7%
9 3015
 
1.5%
6 481
 
0.2%
8 104
 
0.1%
Other Punctuation
ValueCountFrequency (%)
; 11502
35.9%
: 11055
34.5%
, 9388
29.3%
. 57
 
0.2%
/ 11
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
= 26542
99.8%
+ 55
 
0.2%
Space Separator
ValueCountFrequency (%)
45494
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 15824
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11141
100.0%
Close Punctuation
ValueCountFrequency (%)
) 434
100.0%
Open Punctuation
ValueCountFrequency (%)
( 434
100.0%
Control
ValueCountFrequency (%)
174
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 376802
53.5%
Common 326999
46.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 42419
11.3%
F 40898
10.9%
t 39233
10.4%
n 35161
 
9.3%
e 28318
 
7.5%
a 27364
 
7.3%
d 24236
 
6.4%
i 19679
 
5.2%
D 11476
 
3.0%
A 10929
 
2.9%
Other values (37) 97089
25.8%
Common
ValueCountFrequency (%)
0 98753
30.2%
45494
13.9%
2 33085
 
10.1%
5 26640
 
8.1%
= 26542
 
8.1%
_ 15824
 
4.8%
4 15380
 
4.7%
; 11502
 
3.5%
- 11141
 
3.4%
: 11055
 
3.4%
Other values (13) 31583
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 703801
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98753
 
14.0%
45494
 
6.5%
l 42419
 
6.0%
F 40898
 
5.8%
t 39233
 
5.6%
n 35161
 
5.0%
2 33085
 
4.7%
e 28318
 
4.0%
a 27364
 
3.9%
5 26640
 
3.8%
Other values (60) 286436
40.7%

FREQ_TOP_PACK
Real number (ℝ)

MISSING 

Distinct116
Distinct (%)0.4%
Missing21963
Missing (%)42.0%
Infinite0
Infinite (%)0.0%
Mean9.3510309
Minimum1
Maximum174
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size408.9 KiB
2024-04-24T12:55:47.722687image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q312
95-th percentile33
Maximum174
Range173
Interquartile range (IQR)10

Descriptive statistics

Standard deviation12.190022
Coefficient of variation (CV)1.303602
Kurtosis14.252226
Mean9.3510309
Median Absolute Deviation (MAD)4
Skewness3.0509684
Sum283916
Variance148.59664
MonotonicityNot monotonic
2024-04-24T12:55:47.942120image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 6068
 
11.6%
2 3705
 
7.1%
3 2835
 
5.4%
4 2080
 
4.0%
5 1678
 
3.2%
6 1398
 
2.7%
7 1191
 
2.3%
8 1119
 
2.1%
9 917
 
1.8%
10 822
 
1.6%
Other values (106) 8549
 
16.3%
(Missing) 21963
42.0%
ValueCountFrequency (%)
1 6068
11.6%
2 3705
7.1%
3 2835
5.4%
4 2080
 
4.0%
5 1678
 
3.2%
6 1398
 
2.7%
7 1191
 
2.3%
8 1119
 
2.1%
9 917
 
1.8%
10 822
 
1.6%
ValueCountFrequency (%)
174 1
< 0.1%
151 1
< 0.1%
139 1
< 0.1%
136 2
< 0.1%
126 1
< 0.1%
125 2
< 0.1%
124 1
< 0.1%
122 1
< 0.1%
121 2
< 0.1%
120 1
< 0.1%

CHURN
Categorical

Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size408.9 KiB
0.0
42486 
1.0
9838 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters156972
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 42486
81.2%
1.0 9838
 
18.8%
(Missing) 1
 
< 0.1%

Length

2024-04-24T12:55:48.255800image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-24T12:55:48.459712image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 42486
81.2%
1.0 9838
 
18.8%

Most occurring characters

ValueCountFrequency (%)
0 94810
60.4%
. 52324
33.3%
1 9838
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 104648
66.7%
Other Punctuation 52324
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 94810
90.6%
1 9838
 
9.4%
Other Punctuation
ValueCountFrequency (%)
. 52324
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 156972
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 94810
60.4%
. 52324
33.3%
1 9838
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 156972
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 94810
60.4%
. 52324
33.3%
1 9838
 
6.3%

Interactions

2024-04-24T12:55:27.646049image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:54:50.506179image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:54:53.740330image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:54:56.446775image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:54:59.237063image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:02.137408image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:05.287737image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:07.821159image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:10.811847image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:14.414334image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:16.905723image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:19.865343image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:23.100982image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:27.928492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:54:50.720059image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:54:53.886264image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:54:56.614679image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:54:59.432949image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:03.055883image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:05.475634image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:08.099002image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:10.955831image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:14.634277image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:17.049687image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:20.085288image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:23.388463image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:28.194790image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:54:50.976911image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:54:54.021168image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:54:56.762597image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:54:59.697797image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:03.250771image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:05.669518image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:08.300884image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:11.131767image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:14.778240image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:17.205629image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:20.302311image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:23.788338image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:28.461221image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:54:51.300726image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:54:54.233044image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:54:56.961482image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:00.004623image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:03.475641image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:05.900387image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:08.474782image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:11.410212image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:14.982191image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:17.445567image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:20.578241image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:24.117653image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:28.665143image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:54:51.536591image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:54:54.501891image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:54:57.309281image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:00.258476image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:03.717503image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:06.096275image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:08.660677image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:11.908207image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:15.238124image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:17.637520image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:20.834178image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:24.352983image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:28.853433image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:54:51.699498image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:54:54.690801image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:54:57.506169image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:00.443192image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:03.937377image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:06.240196image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:08.851566image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:12.300110image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:15.486063image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:17.813485image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:21.038129image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:24.944067image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:29.182743image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:54:52.047297image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:54:55.127534image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:54:57.736037image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:00.672041image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:04.069302image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:06.380985image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:09.185376image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:12.759994image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:15.686012image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:17.977454image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:21.297827image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:25.210886image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:29.497434image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:54:52.301151image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:54:55.397377image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:54:57.882838image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:00.841942image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:04.249333image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:06.541911image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:09.478210image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:13.278618image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:15.829977image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:18.181382image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:21.517771image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:25.492614image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:29.763235image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:54:52.561003image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:54:55.558285image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:54:58.062734image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:01.103792image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:04.421232image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:06.756766image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:09.641116image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:13.450579image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:15.977937image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:18.545290image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:21.797700image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:25.774664image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:29.979924image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:54:52.836844image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:54:55.698226image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:54:58.273613image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:01.319669image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:04.610124image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:06.986634image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:09.786031image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:13.590541image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:16.161891image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:18.737243image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:22.069632image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:26.469418image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:30.152587image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:54:53.005768image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:54:55.874109image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:54:58.503485image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:01.475807image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:04.814008image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:07.189518image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:10.231994image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:13.730506image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:16.421826image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:18.997177image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:22.357559image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:26.846007image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:30.387769image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:54:53.199636image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:54:56.071991image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:54:58.765331image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:01.619706image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:04.992907image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:07.334438image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:10.427944image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:13.902461image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:16.581784image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:19.261494image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:22.609498image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:27.159691image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:30.685834image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:54:53.500463image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:54:56.262881image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:54:59.026185image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:01.849573image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:05.134846image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:07.509335image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:10.639890image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:14.158398image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:16.761742image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:19.553424image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:22.815970image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-24T12:55:27.426503image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-04-24T12:55:31.093668image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-24T12:55:31.972241image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

user_idREGIONTENUREMONTANTFREQUENCE_RECHREVENUEARPU_SEGMENTFREQUENCEDATA_VOLUMEON_NETORANGETIGOZONE1ZONE2MRGREGULARITYTOP_PACKFREQ_TOP_PACKCHURN
000000bfd7d50f01092811bc0c8d7b0d6fe7c3596FATICKK > 24 month4250.015.04251.01417.017.04.0388.046.01.01.02.0NO54.0On net 200F=Unlimited _call24H8.00.0
100000cb4a5d760de88fecb38e2f71b7bec52e834NaNI 18-21 monthNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNO4.0NaNNaN1.0
200001654a9d9f96303d9969d0a4a851714a4bb57NaNK > 24 month3600.02.01020.0340.02.0NaN90.046.07.0NaNNaNNO17.0On-net 1000F=10MilF;10d1.00.0
300001dd6fa45f7ba044bd5d84937be464ce78ac2DAKARK > 24 month13500.015.013502.04501.018.043804.041.0102.02.0NaNNaNNO62.0Data:1000F=5GB,7d11.00.0
4000028d9e13a595abe061f9b58f3d76ab907850fDAKARK > 24 month1000.01.0985.0328.01.0NaN39.024.0NaNNaNNaNNO11.0Mixt 250F=Unlimited_call24H2.00.0
50000296564272665ccd2925d377e124f3306b01eLOUGAK > 24 month8500.017.09000.03000.018.0NaN252.070.091.0NaNNaNNO62.0MIXT:500F= 2500F on net _2500F off net;2d18.00.0
600002b0ed56e2c199ec8c3021327229afa70f063LOUGAK > 24 monthNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNO2.0NaNNaN0.0
70000313946b6849745963442c6e572d47cd24cedDAKARK > 24 month7000.016.07229.02410.022.01601.077.029.0100.0NaNNaNNO55.0All-net 500F=2000F;5d8.00.0
80000398021ccd3a488fa1a63dee3b2f0d471f9fdDAKARK > 24 month1500.03.01502.0501.012.0NaN2.053.02.0NaNNaNNO31.0NaNNaN0.0
900003d165737109921ebd21f883cb8cff028b626TAMBACOUNDAK > 24 month4000.08.04000.01333.08.0NaN1620.09.0NaNNaNNaNNO45.0On-net 500F_FNF;3d8.00.0
user_idREGIONTENUREMONTANTFREQUENCE_RECHREVENUEARPU_SEGMENTFREQUENCEDATA_VOLUMEON_NETORANGETIGOZONE1ZONE2MRGREGULARITYTOP_PACKFREQ_TOP_PACKCHURN
52315064b0cca8eca9cb5421887eb5135f73d28c483bdKAOLACKK > 24 monthNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNO25.0NaNNaN0.0
52316064b1353e7696690d4210f2ffb5e4063f7aa84c6DAKARK > 24 month500.01.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNO5.0NaNNaN0.0
52317064b15946a12f77fa9041f12de5c171b8c9ef93eTHIESK > 24 month2100.06.02580.0860.07.01208.0718.0NaNNaNNaNNaNNO30.0On-net 500F_FNF;3d3.00.0
52318064b1c1dbafa39581ea78d8c62acbd3264b224e2KAOLACKK > 24 monthNaNNaNNaNNaNNaN487.05.0NaNNaNNaNNaNNO9.0NaNNaN0.0
52319064b1cbdd5b7f611299d0b5bdad5f9e0c50e24dfDAKARK > 24 month3000.06.03000.01000.06.0NaN183.01.0NaNNaNNaNNO23.0All-net 500F=2000F;5d4.00.0
52320064b1e66f1a980abd4ac90846de4e51c857f85d9NaNK > 24 monthNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNO3.0NaNNaN1.0
52321064b2516ec20180480c45e0c9c1e5847f2dac762THIESK > 24 month1200.02.01198.0399.02.0NaN43.019.0NaNNaN1.0NO7.0All-net 500F=2000F;5d2.00.0
52322064b25af59754420bcf31661e4e31585739335b8NaNK > 24 monthNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNO4.0NaNNaN0.0
52323064b2de9946e44fdbd35a5cb584ac4567edbd1b8SAINT-LOUISK > 24 month100.01.0100.033.01.0NaNNaNNaNNaNNaNNaNNO3.0Data: 100 F=40MB,24H1.00.0
52324064b3105f50868fc36e5ed2679e98dcf992a9c48ZIGUINCHORK >NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN